-------
        Here we only create a ``Series``.

        >>> ser = pd.Series([1, 2, 3, 4], index=pd.DatetimeIndex(
        ...                 ['2023-01-01', '2023-01-15', '2023-02-01', '2023-02-15']))
        >>> ser
        2023-01-01    1
        2023-01-15    2
        2023-02-01    3
        2023-02-15    4
        dtype: int64

        Example for ``ffill`` with downsampling (we have fewer dates after resampling):

        >>> ser.resample('MS').ffill()
        2023-01-01    1
        2023-02-01    3
        Freq: MS, dtype: int64

        Example for ``ffill`` with upsampling (fill the new dates with
        the previous value):

        >>> ser.resample('W').ffill()
        2023-01-01    1
        2023-01-08    1
        2023-01-15    2
        2023-01-22    2
        2023-01-29    2
        2023-02-05    3
        2023-02-12    3
        2023-02-19    4
        Freq: W-SUN, dtype: int64

        With upsampling and limiting (only fill the first new date with the
        previous value):

        >>> ser.resample('W').ffill(limit=1)
        2023-01-01    1.0
        2023-01-08    1.0
        2023-01-15    2.0
        2023-01-22    2.0
        2023-01-29    NaN
        2023-02-05    3.0
        2023-02-12    NaN
        2023-02-19    4.0
        Freq: W-SUN, dtype: float64
        Ú